import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from IPython.display import Markdown, display
def printmd(string):
    display(Markdown(string))

# Importing data
df = pd.read_csv('/etlstage/PEE_joint/NUS_modules/CS5340/group_data/GME_stock.csv')
df.head()

	date	open_price	high_price	low_price	close_price	volume	adjclose_price
0	2021-01-28	265.000000	483.000000	112.250000	193.600006	58815800.0	193.600006
1	2021-01-27	354.829987	380.000000	249.000000	347.510010	93396700.0	347.510010
2	2021-01-26	88.559998	150.000000	80.199997	147.979996	178588000.0	147.979996
3	2021-01-25	96.730003	159.179993	61.130001	76.790001	177874000.0	76.790001
4	2021-01-22	42.590000	76.760002	42.320000	65.010002	196784300.0	65.010002

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4773 entries, 0 to 4772
Data columns (total 7 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   date            4773 non-null   object 
 1   open_price      4773 non-null   float64
 2   high_price      4773 non-null   float64
 3   low_price       4773 non-null   float64
 4   close_price     4773 non-null   float64
 5   volume          4773 non-null   float64
 6   adjclose_price  4773 non-null   float64
dtypes: float64(6), object(1)
memory usage: 261.1+ KB

df['date'] = pd.to_datetime(df['date'])
df.set_index('date',inplace=True)
df.head()

	open_price	high_price	low_price	close_price	volume	adjclose_price
date
2021-01-28	265.000000	483.000000	112.250000	193.600006	58815800.0	193.600006
2021-01-27	354.829987	380.000000	249.000000	347.510010	93396700.0	347.510010
2021-01-26	88.559998	150.000000	80.199997	147.979996	178588000.0	147.979996
2021-01-25	96.730003	159.179993	61.130001	76.790001	177874000.0	76.790001
2021-01-22	42.590000	76.760002	42.320000	65.010002	196784300.0	65.010002

# Plotting the open, close, high, low, volume and adjusted close value for the term of 12 months
fig = px.line(df, x=df.index, y=df.columns,
              title='Plot of values for a 12 month period')
fig.update_xaxes(
    dtick="M12",
    tickformat="%b\n%Y")


fig.update_xaxes(rangeslider_visible=True)
fig.show()

# Plotting the total amount traded
df['total_amount_traded'] = df['open_price']*df['volume']

fig = px.line(df, x=df.index, y=df.total_amount_traded,
              title='Plot of total amount traded = open_price * volume')
fig.update_xaxes(
    rangeslider_visible=True,
    rangeselector=dict(
        buttons=list([
            dict(count=1, label="1m", step="month", stepmode="backward"),
            dict(count=6, label="6m", step="month", stepmode="backward"),
            dict(count=1, label="1y", step="year", stepmode="backward"),
            dict(step="all")
        ])
    )
)
fig.show()

# Plotting the moving average for the opening price
df['MA_256'] = df['open_price'].rolling(256).mean()
df[['open_price','MA_256']].plot(figsize=(16,8))

<matplotlib.axes._subplots.AxesSubplot at 0x7f74a3c84f98>